Related papers: Neighborhoods and Functionality in Metals
Identifying possible clusters in datasets and estimating their overall modularity are central tasks in pattern recognition. In the present work, concepts and methodologies are described for performing these tasks while considering only the…
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bonding but often do not include local coordination…
Discovering the low-energy conformations of a molecule is of great interest to computational chemists, with applications in {\em in silico} materials design and drug discovery. In this paper, we propose a variable neighbourhood search…
Using the embedded-atom method, the structure of small copper clusters on Au(111) electrodes has been investigated both by static and dynamic calculations. By varying the size of roughly circular clusters, the edge energy per atom is…
This paper proposes a new method to analyze the spatial structure of urban systems using ideas from fractals. Regarding a system of cities as a set of "particles" distributed randomly on a triangular lattice, we construct a spatial…
We study the energetic stability and structural features of bimetallic metal-organic frameworks. Such heterometallic MOFs, which can result from partial substitutions between two types of cations, can have specific physical or chemical…
Lattice models parameterized using first-principles calculations constitute an effective framework to simulate the thermodynamic behavior of physical systems. The cluster expansion method is a flexible lattice-based method used extensively…
The theoretical formalism of the local density approximation (LDA) to density functional theory (DFT) has been used to study the electronic and geometric structures of SimCn (1<=m, n<=4;n<=m) clusters. An all electron 6-311++G** basis set…
Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but…
Communities are clusters of nodes with a higher than average density of internal connections. Their detection is of great relevance to better understand the structure and hierarchies present in a network. Modularity has become a standard…
In colloid and nanoparticle chemistry, particle size, shape, crystallinity, surface morphology and composition are controlled by employing the mechanisms of burst nucleation, diffusional growth, aggregation, or their combinations. Here we…
In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a…
We present an approach to study functional segregation and integration in the living brain based on community structure decomposition determined by maximum modularity. We demonstrate this method with a network derived from functional…
Morphology and its stability are essential features to address physicochemical properties of metallic nanoparticles. By means of Molecular Dynamics based simulations we show a complex dependence on the size and material of common structural…
Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more.…
We explore whether the topology of energy landscapes in chemical systems obeys any rules and what these rules are. To answer this and related questions we use several tools: (i)Reduced energy surface and its density of states, (ii)…
A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found…
Reactivity of Aluminum Clusters has been found to exhibit size sensitive variations. This work is motivated by a recent report1 predicting higher reactivity of melted Aluminum clusters towards the N2 molecule as compared to the non-melted…
Size differences of approx. 20% between red (metal-rich) and blue (metal-poor) sub-populations of globular clusters have been observed, generating an ongoing debate as to weather these originate from projection effects or the difference in…